Overview

Dataset statistics

Number of variables18
Number of observations181060
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory172.9 MiB
Average record size in memory1001.3 B

Variable types

Text5
Categorical6
Numeric7

Alerts

State has constant value "WA"Constant
DOL Vehicle ID has unique valuesUnique
Electric Range has 94567 (52.2%) zerosZeros

Reproduction

Analysis started2024-06-05 15:49:21.695427
Analysis finished2024-06-05 15:50:11.492149
Duration49.8 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct11055
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
2024-06-05T11:50:11.877302image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1810600
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2063 ?
Unique (%)1.1%

Sample

1st rowWAUTPBFF4H
2nd rowWAUUPBFF2J
3rd row5YJSA1E22H
4th row1C4JJXP62M
5th row5YJ3E1EC9L
ValueCountFrequency (%)
7saygdee7p 1242
 
0.7%
7saygdee6p 1241
 
0.7%
7saygdee8p 1197
 
0.7%
7saygdee5p 1191
 
0.7%
7saygdeexp 1181
 
0.7%
7saygdee9p 1171
 
0.6%
7saygdee0p 1168
 
0.6%
7saygdee2p 1166
 
0.6%
7saygdee3p 1155
 
0.6%
7saygdee4p 1128
 
0.6%
Other values (11045) 169220
93.5%
2024-06-05T11:50:12.377474image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 163247
 
9.0%
1 128995
 
7.1%
A 115233
 
6.4%
Y 101737
 
5.6%
P 91247
 
5.0%
J 88546
 
4.9%
5 83789
 
4.6%
3 73880
 
4.1%
D 71481
 
3.9%
G 70364
 
3.9%
Other values (24) 822081
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1810600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 163247
 
9.0%
1 128995
 
7.1%
A 115233
 
6.4%
Y 101737
 
5.6%
P 91247
 
5.0%
J 88546
 
4.9%
5 83789
 
4.6%
3 73880
 
4.1%
D 71481
 
3.9%
G 70364
 
3.9%
Other values (24) 822081
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1810600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 163247
 
9.0%
1 128995
 
7.1%
A 115233
 
6.4%
Y 101737
 
5.6%
P 91247
 
5.0%
J 88546
 
4.9%
5 83789
 
4.6%
3 73880
 
4.1%
D 71481
 
3.9%
G 70364
 
3.9%
Other values (24) 822081
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1810600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 163247
 
9.0%
1 128995
 
7.1%
A 115233
 
6.4%
Y 101737
 
5.6%
P 91247
 
5.0%
J 88546
 
4.9%
5 83789
 
4.6%
3 73880
 
4.1%
D 71481
 
3.9%
G 70364
 
3.9%
Other values (24) 822081
45.4%

County
Categorical

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
King
94460 
Snohomish
21439 
Pierce
14043 
Clark
10667 
Thurston
 
6600
Other values (34)
33851 

Length

Max length12
Median length4
Mean length5.4678946
Min length4

Characters and Unicode

Total characters990017
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKing
2nd rowThurston
3rd rowThurston
4th rowThurston
5th rowYakima

Common Values

ValueCountFrequency (%)
King 94460
52.2%
Snohomish 21439
 
11.8%
Pierce 14043
 
7.8%
Clark 10667
 
5.9%
Thurston 6600
 
3.6%
Kitsap 5956
 
3.3%
Spokane 4671
 
2.6%
Whatcom 4331
 
2.4%
Benton 2183
 
1.2%
Skagit 1968
 
1.1%
Other values (29) 14742
 
8.1%

Length

2024-06-05T11:50:12.536822image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
king 94460
51.6%
snohomish 21439
 
11.7%
pierce 14043
 
7.7%
clark 10667
 
5.8%
thurston 6600
 
3.6%
kitsap 5956
 
3.3%
spokane 4671
 
2.6%
whatcom 4331
 
2.4%
benton 2183
 
1.2%
skagit 1968
 
1.1%
Other values (32) 16832
 
9.2%

Most occurring characters

ValueCountFrequency (%)
i 144278
14.6%
n 141556
14.3%
K 101353
10.2%
g 97069
9.8%
o 64863
 
6.6%
h 55289
 
5.6%
a 44954
 
4.5%
s 40525
 
4.1%
e 39420
 
4.0%
r 35537
 
3.6%
Other values (32) 225173
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 990017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 144278
14.6%
n 141556
14.3%
K 101353
10.2%
g 97069
9.8%
o 64863
 
6.6%
h 55289
 
5.6%
a 44954
 
4.5%
s 40525
 
4.1%
e 39420
 
4.0%
r 35537
 
3.6%
Other values (32) 225173
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 990017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 144278
14.6%
n 141556
14.3%
K 101353
10.2%
g 97069
9.8%
o 64863
 
6.6%
h 55289
 
5.6%
a 44954
 
4.5%
s 40525
 
4.1%
e 39420
 
4.0%
r 35537
 
3.6%
Other values (32) 225173
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 990017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 144278
14.6%
n 141556
14.3%
K 101353
10.2%
g 97069
9.8%
o 64863
 
6.6%
h 55289
 
5.6%
a 44954
 
4.5%
s 40525
 
4.1%
e 39420
 
4.0%
r 35537
 
3.6%
Other values (32) 225173
22.7%

City
Text

Distinct468
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
2024-06-05T11:50:12.906832image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length24
Median length17
Mean length8.2031481
Min length3

Characters and Unicode

Total characters1485262
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)< 0.1%

Sample

1st rowSeattle
2nd rowOlympia
3rd rowLacey
4th rowTenino
5th rowYakima
ValueCountFrequency (%)
seattle 30045
 
14.3%
bellevue 9116
 
4.3%
redmond 6568
 
3.1%
vancouver 6329
 
3.0%
bothell 5961
 
2.8%
kirkland 5465
 
2.6%
sammamish 5350
 
2.6%
renton 5104
 
2.4%
island 5053
 
2.4%
olympia 4397
 
2.1%
Other values (489) 126388
60.2%
2024-06-05T11:50:13.396114image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 202652
13.6%
a 144392
 
9.7%
l 131972
 
8.9%
t 103309
 
7.0%
n 98271
 
6.6%
o 87475
 
5.9%
r 61255
 
4.1%
i 59077
 
4.0%
S 51169
 
3.4%
d 49566
 
3.3%
Other values (42) 496124
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1485262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 202652
13.6%
a 144392
 
9.7%
l 131972
 
8.9%
t 103309
 
7.0%
n 98271
 
6.6%
o 87475
 
5.9%
r 61255
 
4.1%
i 59077
 
4.0%
S 51169
 
3.4%
d 49566
 
3.3%
Other values (42) 496124
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1485262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 202652
13.6%
a 144392
 
9.7%
l 131972
 
8.9%
t 103309
 
7.0%
n 98271
 
6.6%
o 87475
 
5.9%
r 61255
 
4.1%
i 59077
 
4.0%
S 51169
 
3.4%
d 49566
 
3.3%
Other values (42) 496124
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1485262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 202652
13.6%
a 144392
 
9.7%
l 131972
 
8.9%
t 103309
 
7.0%
n 98271
 
6.6%
o 87475
 
5.9%
r 61255
 
4.1%
i 59077
 
4.0%
S 51169
 
3.4%
d 49566
 
3.3%
Other values (42) 496124
33.4%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
WA
181060 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters362120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 181060
100.0%

Length

2024-06-05T11:50:13.558021image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T11:50:13.679933image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
wa 181060
100.0%

Most occurring characters

ValueCountFrequency (%)
W 181060
50.0%
A 181060
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 181060
50.0%
A 181060
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 181060
50.0%
A 181060
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 181060
50.0%
A 181060
50.0%

Postal Code
Real number (ℝ)

Distinct543
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98260.63
Minimum98001
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:13.781363image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98007
Q198052
median98122
Q398371
95-th percentile98941
Maximum99403
Range1402
Interquartile range (IQR)319

Descriptive statistics

Standard deviation303.62078
Coefficient of variation (CV)0.0030899535
Kurtosis3.0395002
Mean98260.63
Median Absolute Deviation (MAD)99
Skewness1.8108531
Sum1.779107 × 1010
Variance92185.576
MonotonicityNot monotonic
2024-06-05T11:50:13.928127image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 4637
 
2.6%
98012 3392
 
1.9%
98033 3135
 
1.7%
98188 3012
 
1.7%
98006 2908
 
1.6%
98004 2885
 
1.6%
98115 2758
 
1.5%
98074 2564
 
1.4%
98072 2508
 
1.4%
98034 2384
 
1.3%
Other values (533) 150877
83.3%
ValueCountFrequency (%)
98001 764
 
0.4%
98002 278
 
0.2%
98003 558
 
0.3%
98004 2885
1.6%
98005 1326
0.7%
98006 2908
1.6%
98007 973
 
0.5%
98008 1509
0.8%
98010 383
 
0.2%
98011 1168
0.6%
ValueCountFrequency (%)
99403 62
 
< 0.1%
99402 12
 
< 0.1%
99371 1
 
< 0.1%
99362 349
0.2%
99361 10
 
< 0.1%
99360 8
 
< 0.1%
99357 22
 
< 0.1%
99356 1
 
< 0.1%
99354 289
0.2%
99353 226
0.1%

Model Year
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.5833
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:14.066207image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12019
median2022
Q32023
95-th percentile2024
Maximum2024
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9910702
Coefficient of variation (CV)0.0014803004
Kurtosis0.64696828
Mean2020.5833
Median Absolute Deviation (MAD)1
Skewness-1.1490375
Sum3.6584681 × 108
Variance8.946501
MonotonicityNot monotonic
2024-06-05T11:50:14.210501image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 58322
32.2%
2022 27855
15.4%
2021 18966
 
10.5%
2018 14255
 
7.9%
2020 11820
 
6.5%
2019 10895
 
6.0%
2024 9787
 
5.4%
2017 8565
 
4.7%
2016 5509
 
3.0%
2015 4802
 
2.7%
Other values (12) 10284
 
5.7%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 5
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 20
 
< 0.1%
2010 23
 
< 0.1%
2011 770
0.4%
2012 1599
0.9%
ValueCountFrequency (%)
2024 9787
 
5.4%
2023 58322
32.2%
2022 27855
15.4%
2021 18966
 
10.5%
2020 11820
 
6.5%
2019 10895
 
6.0%
2018 14255
 
7.9%
2017 8565
 
4.7%
2016 5509
 
3.0%
2015 4802
 
2.7%

Make
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 MiB
TESLA
80627 
NISSAN
14024 
CHEVROLET
13839 
FORD
9503 
BMW
 
7666
Other values (35)
55401 

Length

Max length20
Median length14
Mean length5.5585386
Min length3

Characters and Unicode

Total characters1006429
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAUDI
2nd rowAUDI
3rd rowTESLA
4th rowJEEP
5th rowTESLA

Common Values

ValueCountFrequency (%)
TESLA 80627
44.5%
NISSAN 14024
 
7.7%
CHEVROLET 13839
 
7.6%
FORD 9503
 
5.2%
BMW 7666
 
4.2%
KIA 7633
 
4.2%
TOYOTA 6486
 
3.6%
VOLKSWAGEN 5153
 
2.8%
JEEP 4679
 
2.6%
HYUNDAI 4553
 
2.5%
Other values (30) 26897
 
14.9%

Length

2024-06-05T11:50:14.390513image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 80627
44.5%
nissan 14024
 
7.7%
chevrolet 13839
 
7.6%
ford 9503
 
5.2%
bmw 7666
 
4.2%
kia 7633
 
4.2%
toyota 6486
 
3.6%
volkswagen 5153
 
2.8%
jeep 4679
 
2.6%
hyundai 4553
 
2.5%
Other values (36) 27011
 
14.9%

Most occurring characters

ValueCountFrequency (%)
E 136110
13.5%
A 132749
13.2%
S 124527
12.4%
T 110374
11.0%
L 109975
10.9%
O 53930
 
5.4%
N 46378
 
4.6%
I 45597
 
4.5%
R 39161
 
3.9%
V 32024
 
3.2%
Other values (18) 175604
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1006429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 136110
13.5%
A 132749
13.2%
S 124527
12.4%
T 110374
11.0%
L 109975
10.9%
O 53930
 
5.4%
N 46378
 
4.6%
I 45597
 
4.5%
R 39161
 
3.9%
V 32024
 
3.2%
Other values (18) 175604
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1006429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 136110
13.5%
A 132749
13.2%
S 124527
12.4%
T 110374
11.0%
L 109975
10.9%
O 53930
 
5.4%
N 46378
 
4.6%
I 45597
 
4.5%
R 39161
 
3.9%
V 32024
 
3.2%
Other values (18) 175604
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1006429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 136110
13.5%
A 132749
13.2%
S 124527
12.4%
T 110374
11.0%
L 109975
10.9%
O 53930
 
5.4%
N 46378
 
4.6%
I 45597
 
4.5%
R 39161
 
3.9%
V 32024
 
3.2%
Other values (18) 175604
17.4%

Model
Text

Distinct143
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.3 MiB
2024-06-05T11:50:14.726394image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.4000221
Min length2

Characters and Unicode

Total characters1158788
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowA3
2nd rowA3
3rd rowMODEL S
4th rowWRANGLER
5th rowMODEL 3
ValueCountFrequency (%)
model 80579
28.0%
y 36937
12.8%
3 30065
 
10.4%
leaf 13343
 
4.6%
bolt 8748
 
3.0%
s 7706
 
2.7%
ev 7201
 
2.5%
x 5871
 
2.0%
prime 5092
 
1.8%
volt 4785
 
1.7%
Other values (140) 87430
30.4%
2024-06-05T11:50:15.243517image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 136115
11.7%
L 119522
 
10.3%
O 114502
 
9.9%
106697
 
9.2%
M 94683
 
8.2%
D 88521
 
7.6%
A 49047
 
4.2%
Y 40033
 
3.5%
R 39927
 
3.4%
I 39439
 
3.4%
Other values (28) 330302
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1158788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 136115
11.7%
L 119522
 
10.3%
O 114502
 
9.9%
106697
 
9.2%
M 94683
 
8.2%
D 88521
 
7.6%
A 49047
 
4.2%
Y 40033
 
3.5%
R 39927
 
3.4%
I 39439
 
3.4%
Other values (28) 330302
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1158788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 136115
11.7%
L 119522
 
10.3%
O 114502
 
9.9%
106697
 
9.2%
M 94683
 
8.2%
D 88521
 
7.6%
A 49047
 
4.2%
Y 40033
 
3.5%
R 39927
 
3.4%
I 39439
 
3.4%
Other values (28) 330302
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1158788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 136115
11.7%
L 119522
 
10.3%
O 114502
 
9.9%
106697
 
9.2%
M 94683
 
8.2%
D 88521
 
7.6%
A 49047
 
4.2%
Y 40033
 
3.5%
R 39927
 
3.4%
I 39439
 
3.4%
Other values (28) 330302
28.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
Battery Electric Vehicle (BEV)
141707 
Plug-in Hybrid Electric Vehicle (PHEV)
39353 

Length

Max length38
Median length30
Mean length31.738783
Min length30

Characters and Unicode

Total characters5746624
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlug-in Hybrid Electric Vehicle (PHEV)
2nd rowPlug-in Hybrid Electric Vehicle (PHEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowPlug-in Hybrid Electric Vehicle (PHEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 141707
78.3%
Plug-in Hybrid Electric Vehicle (PHEV) 39353
 
21.7%

Length

2024-06-05T11:50:15.435072image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T11:50:15.576072image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
electric 181060
23.7%
vehicle 181060
23.7%
battery 141707
18.6%
bev 141707
18.6%
plug-in 39353
 
5.2%
hybrid 39353
 
5.2%
phev 39353
 
5.2%

Most occurring characters

ValueCountFrequency (%)
e 684887
11.9%
582533
10.1%
c 543180
9.5%
t 464474
 
8.1%
i 440826
 
7.7%
l 401473
 
7.0%
V 362120
 
6.3%
r 362120
 
6.3%
E 362120
 
6.3%
B 283414
 
4.9%
Other values (13) 1259477
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5746624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 684887
11.9%
582533
10.1%
c 543180
9.5%
t 464474
 
8.1%
i 440826
 
7.7%
l 401473
 
7.0%
V 362120
 
6.3%
r 362120
 
6.3%
E 362120
 
6.3%
B 283414
 
4.9%
Other values (13) 1259477
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5746624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 684887
11.9%
582533
10.1%
c 543180
9.5%
t 464474
 
8.1%
i 440826
 
7.7%
l 401473
 
7.0%
V 362120
 
6.3%
r 362120
 
6.3%
E 362120
 
6.3%
B 283414
 
4.9%
Other values (13) 1259477
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5746624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 684887
11.9%
582533
10.1%
c 543180
9.5%
t 464474
 
8.1%
i 440826
 
7.7%
l 401473
 
7.0%
V 362120
 
6.3%
r 362120
 
6.3%
E 362120
 
6.3%
B 283414
 
4.9%
Other values (13) 1259477
21.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.8 MiB
Eligibility unknown as battery range has not been researched
94567 
Clean Alternative Fuel Vehicle Eligible
66647 
Not eligible due to low battery range
19846 

Length

Max length60
Median length60
Mean length49.749006
Min length37

Characters and Unicode

Total characters9007555
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot eligible due to low battery range
2nd rowNot eligible due to low battery range
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowNot eligible due to low battery range
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 94567
52.2%
Clean Alternative Fuel Vehicle Eligible 66647
36.8%
Not eligible due to low battery range 19846
 
11.0%

Length

2024-06-05T11:50:15.711895image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-05T11:50:15.849265image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
ValueCountFrequency (%)
not 114413
 
8.6%
battery 114413
 
8.6%
range 114413
 
8.6%
eligibility 94567
 
7.1%
unknown 94567
 
7.1%
been 94567
 
7.1%
researched 94567
 
7.1%
has 94567
 
7.1%
as 94567
 
7.1%
eligible 86493
 
6.5%
Other values (7) 326126
24.6%

Most occurring characters

ValueCountFrequency (%)
e 1227728
13.6%
1142200
12.7%
n 720542
 
8.0%
i 684548
 
7.6%
l 648554
 
7.2%
a 645821
 
7.2%
t 590946
 
6.6%
r 484607
 
5.4%
b 390040
 
4.3%
g 295473
 
3.3%
Other values (16) 2177096
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9007555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1227728
13.6%
1142200
12.7%
n 720542
 
8.0%
i 684548
 
7.6%
l 648554
 
7.2%
a 645821
 
7.2%
t 590946
 
6.6%
r 484607
 
5.4%
b 390040
 
4.3%
g 295473
 
3.3%
Other values (16) 2177096
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9007555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1227728
13.6%
1142200
12.7%
n 720542
 
8.0%
i 684548
 
7.6%
l 648554
 
7.2%
a 645821
 
7.2%
t 590946
 
6.6%
r 484607
 
5.4%
b 390040
 
4.3%
g 295473
 
3.3%
Other values (16) 2177096
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9007555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1227728
13.6%
1142200
12.7%
n 720542
 
8.0%
i 684548
 
7.6%
l 648554
 
7.2%
a 645821
 
7.2%
t 590946
 
6.6%
r 484607
 
5.4%
b 390040
 
4.3%
g 295473
 
3.3%
Other values (16) 2177096
24.2%

Electric Range
Real number (ℝ)

ZEROS 

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.80929
Minimum0
Maximum337
Zeros94567
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:16.067175image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q375
95-th percentile259
Maximum337
Range337
Interquartile range (IQR)75

Descriptive statistics

Standard deviation91.386216
Coefficient of variation (CV)1.5808223
Kurtosis0.69151309
Mean57.80929
Median Absolute Deviation (MAD)0
Skewness1.476434
Sum10466950
Variance8351.4405
MonotonicityNot monotonic
2024-06-05T11:50:16.279292image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94567
52.2%
215 6375
 
3.5%
25 4171
 
2.3%
220 4064
 
2.2%
32 4049
 
2.2%
238 3901
 
2.2%
84 3888
 
2.1%
21 3676
 
2.0%
38 2464
 
1.4%
19 2459
 
1.4%
Other values (93) 51446
28.4%
ValueCountFrequency (%)
0 94567
52.2%
6 936
 
0.5%
8 37
 
< 0.1%
9 20
 
< 0.1%
10 170
 
0.1%
11 3
 
< 0.1%
12 169
 
0.1%
13 357
 
0.2%
14 1106
 
0.6%
15 88
 
< 0.1%
ValueCountFrequency (%)
337 75
 
< 0.1%
330 329
 
0.2%
322 1677
0.9%
308 509
 
0.3%
293 447
 
0.2%
291 2367
1.3%
289 655
 
0.4%
270 277
 
0.2%
266 1414
0.8%
265 129
 
0.1%
Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.5 MiB
41
 
11727
45
 
10937
48
 
10003
1
 
7907
11
 
7761
Other values (44)
132725 

Length

Max length2
Median length2
Mean length1.8587927
Min length1

Characters and Unicode

Total characters336553
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34
2nd row22
3rd row22
4th row20
5th row14

Common Values

ValueCountFrequency (%)
41 11727
 
6.5%
45 10937
 
6.0%
48 10003
 
5.5%
1 7907
 
4.4%
11 7761
 
4.3%
5 7755
 
4.3%
36 7523
 
4.2%
46 7005
 
3.9%
43 6656
 
3.7%
37 5313
 
2.9%
Other values (39) 98473
54.4%

Length

2024-06-05T11:50:16.482479image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41 11727
 
6.5%
45 10937
 
6.0%
48 10003
 
5.5%
1 7907
 
4.4%
11 7761
 
4.3%
5 7755
 
4.3%
36 7523
 
4.2%
46 7005
 
3.9%
43 6656
 
3.7%
37 5313
 
2.9%
Other values (39) 98473
54.4%

Most occurring characters

ValueCountFrequency (%)
4 78380
23.3%
1 60353
17.9%
3 54678
16.2%
2 43766
13.0%
5 23576
 
7.0%
8 20832
 
6.2%
6 20750
 
6.2%
7 15086
 
4.5%
0 10758
 
3.2%
9 8374
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 78380
23.3%
1 60353
17.9%
3 54678
16.2%
2 43766
13.0%
5 23576
 
7.0%
8 20832
 
6.2%
6 20750
 
6.2%
7 15086
 
4.5%
0 10758
 
3.2%
9 8374
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 78380
23.3%
1 60353
17.9%
3 54678
16.2%
2 43766
13.0%
5 23576
 
7.0%
8 20832
 
6.2%
6 20750
 
6.2%
7 15086
 
4.5%
0 10758
 
3.2%
9 8374
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 78380
23.3%
1 60353
17.9%
3 54678
16.2%
2 43766
13.0%
5 23576
 
7.0%
8 20832
 
6.2%
6 20750
 
6.2%
7 15086
 
4.5%
0 10758
 
3.2%
9 8374
 
2.5%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct181060
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.214364 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:16.662550image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.1065603 × 108
Q11.8307987 × 108
median2.2909638 × 108
Q32.5613753 × 108
95-th percentile3.3497607 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)73057665

Descriptive statistics

Standard deviation75288872
Coefficient of variation (CV)0.34000224
Kurtosis3.5577686
Mean2.214364 × 108
Median Absolute Deviation (MAD)30878456
Skewness0.56994031
Sum4.0093274 × 1013
Variance5.6684143 × 1015
MonotonicityNot monotonic
2024-06-05T11:50:16.864559image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235085336 1
 
< 0.1%
293774533 1
 
< 0.1%
103609999 1
 
< 0.1%
219197338 1
 
< 0.1%
348531522 1
 
< 0.1%
244538157 1
 
< 0.1%
171454195 1
 
< 0.1%
172744029 1
 
< 0.1%
193623503 1
 
< 0.1%
222389436 1
 
< 0.1%
Other values (181050) 181050
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
61092 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
478909938 1
< 0.1%
Distinct542
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Memory size16.2 MiB
2024-06-05T11:50:18.116973image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length33
Median length32
Mean length29.042263
Min length25

Characters and Unicode

Total characters5258247
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowPOINT (-122.374105 47.54468)
2nd rowPOINT (-122.943445 47.059252)
3rd rowPOINT (-122.78083 47.083975)
4th rowPOINT (-122.85403 46.856085)
5th rowPOINT (-120.524012 46.5973939)
ValueCountFrequency (%)
point 181055
33.3%
47.6705374 4637
 
0.9%
122.1207376 4637
 
0.9%
122.1873 3392
 
0.6%
47.820245 3392
 
0.6%
122.20264 3135
 
0.6%
47.6785 3135
 
0.6%
122.271716 3012
 
0.6%
47.452837 3012
 
0.6%
122.16937 2908
 
0.5%
Other values (1074) 330850
60.9%
2024-06-05T11:50:18.645752image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 552567
 
10.5%
1 419714
 
8.0%
4 372337
 
7.1%
7 371186
 
7.1%
. 362110
 
6.9%
362110
 
6.9%
5 274787
 
5.2%
6 253752
 
4.8%
3 251629
 
4.8%
8 217958
 
4.1%
Other values (10) 1820097
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5258247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 552567
 
10.5%
1 419714
 
8.0%
4 372337
 
7.1%
7 371186
 
7.1%
. 362110
 
6.9%
362110
 
6.9%
5 274787
 
5.2%
6 253752
 
4.8%
3 251629
 
4.8%
8 217958
 
4.1%
Other values (10) 1820097
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5258247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 552567
 
10.5%
1 419714
 
8.0%
4 372337
 
7.1%
7 371186
 
7.1%
. 362110
 
6.9%
362110
 
6.9%
5 274787
 
5.2%
6 253752
 
4.8%
3 251629
 
4.8%
8 217958
 
4.1%
Other values (10) 1820097
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5258247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 552567
 
10.5%
1 419714
 
8.0%
4 372337
 
7.1%
7 371186
 
7.1%
. 362110
 
6.9%
362110
 
6.9%
5 274787
 
5.2%
6 253752
 
4.8%
3 251629
 
4.8%
8 217958
 
4.1%
Other values (10) 1820097
34.6%

Latitude
Real number (ℝ)

Distinct542
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47.46421
Minimum45.58359
Maximum48.992052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:18.802012image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum45.58359
5-th percentile45.74169
Q147.358111
median47.610365
Q347.715668
95-th percentile48.198211
Maximum48.992052
Range3.4084621
Interquartile range (IQR)0.3575573

Descriptive statistics

Standard deviation0.61023076
Coefficient of variation (CV)0.012856651
Kurtosis2.9075598
Mean47.46421
Median Absolute Deviation (MAD)0.157528
Skewness-1.3883396
Sum8593632.5
Variance0.37238158
MonotonicityNot monotonic
2024-06-05T11:50:18.953423image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.6705374 4637
 
2.6%
47.820245 3392
 
1.9%
47.6785 3135
 
1.7%
47.452837 3012
 
1.7%
47.571015 2908
 
1.6%
47.619252 2885
 
1.6%
47.67949 2758
 
1.5%
47.6285782 2564
 
1.4%
47.75855 2508
 
1.4%
47.71124 2384
 
1.3%
Other values (532) 150872
83.3%
ValueCountFrequency (%)
45.58359 596
 
0.3%
45.604689 735
0.4%
45.6092439 1637
0.9%
45.6174915 9
 
< 0.1%
45.620105 394
 
0.2%
45.6228 545
 
0.3%
45.6365338 310
 
0.2%
45.638545 617
 
0.3%
45.6397193 11
 
< 0.1%
45.641205 247
 
0.1%
ValueCountFrequency (%)
48.9920521 1
 
< 0.1%
48.9910612 57
 
< 0.1%
48.9769756 14
 
< 0.1%
48.953176 407
0.2%
48.952558 28
 
< 0.1%
48.9461196 229
0.1%
48.9395869 31
 
< 0.1%
48.9335881 19
 
< 0.1%
48.9260491 10
 
< 0.1%
48.9119603 2
 
< 0.1%

Longitude
Real number (ℝ)

Distinct541
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-122.08348
Minimum-124.61408
Maximum-117.05952
Zeros0
Zeros (%)0.0%
Negative181055
Negative (%)> 99.9%
Memory size2.8 MiB
2024-06-05T11:50:19.101096image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum-124.61408
5-th percentile-122.87325
Q1-122.39552
median-122.28397
Q3-122.14738
95-th percentile-119.47376
Maximum-117.05952
Range7.5545593
Interquartile range (IQR)0.248134

Descriptive statistics

Standard deviation1.0171684
Coefficient of variation (CV)-0.0083317442
Kurtosis12.842173
Mean-122.08348
Median Absolute Deviation (MAD)0.132308
Skewness3.5319849
Sum-22103825
Variance1.0346315
MonotonicityNot monotonic
2024-06-05T11:50:19.245077image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.1207376 4637
 
2.6%
-122.1873 3392
 
1.9%
-122.20264 3135
 
1.7%
-122.271716 3012
 
1.7%
-122.16937 2908
 
1.6%
-122.202397 2885
 
1.6%
-122.3185 2758
 
1.5%
-122.0313266 2564
 
1.4%
-122.151665 2508
 
1.4%
-122.209285 2384
 
1.3%
Other values (531) 150872
83.3%
ValueCountFrequency (%)
-124.6140781 6
 
< 0.1%
-124.3746458 3
 
< 0.1%
-124.3405386 2
 
< 0.1%
-124.2846109 3
 
< 0.1%
-124.264464 2
 
< 0.1%
-124.25311 12
 
< 0.1%
-124.1965652 25
 
< 0.1%
-124.1853792 3
 
< 0.1%
-124.17386 3
 
< 0.1%
-124.1599804 134
0.1%
ValueCountFrequency (%)
-117.0595188 12
 
< 0.1%
-117.075672 45
 
< 0.1%
-117.0774369 5
 
< 0.1%
-117.078955 22
 
< 0.1%
-117.08094 62
 
< 0.1%
-117.0888411 3
 
< 0.1%
-117.09883 219
0.1%
-117.1284737 3
 
< 0.1%
-117.1286861 53
 
< 0.1%
-117.1403337 5
 
< 0.1%
Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.9 MiB
2024-06-05T11:50:19.434161image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.313907
Min length10

Characters and Unicode

Total characters8023476
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2nd rowPUGET SOUND ENERGY INC
3rd rowPUGET SOUND ENERGY INC
4th rowPUGET SOUND ENERGY INC
5th rowPACIFICORP
ValueCountFrequency (%)
of 170257
12.6%
159600
11.8%
wa 111559
 
8.2%
tacoma 110105
 
8.1%
energy 108942
 
8.0%
sound 108942
 
8.0%
puget 107975
 
8.0%
inc||city 67180
 
5.0%
power 39621
 
2.9%
inc 36986
 
2.7%
Other values (111) 333876
24.6%
2024-06-05T11:50:19.769095image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1173983
14.6%
O 589488
 
7.3%
N 571183
 
7.1%
T 555765
 
6.9%
A 541311
 
6.7%
E 524908
 
6.5%
I 437868
 
5.5%
C 437610
 
5.5%
Y 293560
 
3.7%
U 281849
 
3.5%
Other values (26) 2615951
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8023476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1173983
14.6%
O 589488
 
7.3%
N 571183
 
7.1%
T 555765
 
6.9%
A 541311
 
6.7%
E 524908
 
6.5%
I 437868
 
5.5%
C 437610
 
5.5%
Y 293560
 
3.7%
U 281849
 
3.5%
Other values (26) 2615951
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8023476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1173983
14.6%
O 589488
 
7.3%
N 571183
 
7.1%
T 555765
 
6.9%
A 541311
 
6.7%
E 524908
 
6.5%
I 437868
 
5.5%
C 437610
 
5.5%
Y 293560
 
3.7%
U 281849
 
3.5%
Other values (26) 2615951
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8023476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1173983
14.6%
O 589488
 
7.3%
N 571183
 
7.1%
T 555765
 
6.9%
A 541311
 
6.7%
E 524908
 
6.5%
I 437868
 
5.5%
C 437610
 
5.5%
Y 293560
 
3.7%
U 281849
 
3.5%
Other values (26) 2615951
32.6%

2020 Census Tract
Real number (ℝ)

Distinct1767
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3039817 × 1010
Minimum5.300195 × 1010
Maximum5.307794 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-06-05T11:50:19.916209image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum5.300195 × 1010
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.303303 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.307794 × 1010
Range75989907
Interquartile range (IQR)20063204

Descriptive statistics

Standard deviation16218591
Coefficient of variation (CV)0.00030578143
Kurtosis-0.43822876
Mean5.3039817 × 1010
Median Absolute Deviation (MAD)27504
Skewness0.28115248
Sum9.6033893 × 1015
Variance2.630427 × 1014
MonotonicityNot monotonic
2024-06-05T11:50:20.113220image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10102551
 
1.4%
5.30330285 × 1010931
 
0.5%
5.30330262 × 1010861
 
0.5%
5.303303232 × 1010832
 
0.5%
5.30330093 × 1010698
 
0.4%
5.30670112 × 1010692
 
0.4%
5.303303232 × 1010604
 
0.3%
5.306105211 × 1010593
 
0.3%
5.303303222 × 1010582
 
0.3%
5.303302501 × 1010574
 
0.3%
Other values (1757) 172142
95.1%
ValueCountFrequency (%)
5.30019501 × 101018
< 0.1%
5.30019502 × 10105
 
< 0.1%
5.30019503 × 10103
 
< 0.1%
5.30019503 × 10103
 
< 0.1%
5.30019503 × 10106
 
< 0.1%
5.30019504 × 10105
 
< 0.1%
5.30019505 × 101011
 
< 0.1%
5.30039601 × 101028
< 0.1%
5.30039602 × 101023
< 0.1%
5.30039603 × 10102
 
< 0.1%
ValueCountFrequency (%)
5.307794001 × 10106
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10102
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10108
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101037
< 0.1%
5.30770032 × 101043
< 0.1%
5.30770031 × 101021
< 0.1%
5.3077003 × 10107
 
< 0.1%

Interactions

2024-06-05T11:49:52.742180image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:26.415892image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:33.254386image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:36.721959image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:40.322580image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:44.500083image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:47.805654image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:54.936774image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:27.585663image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:33.834931image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:37.340448image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.111370image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.147256image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:49.757783image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:57.695589image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:28.244867image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:33.959106image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:37.470431image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.242680image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.282858image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:49.973097image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:50:00.070049image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:28.921983image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:34.100474image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:37.599238image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.371620image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.420789image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:50.200684image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:50:02.008825image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:29.487797image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:34.222348image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:37.725295image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.497589image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.558254image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:50.349400image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:50:04.846627image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:30.054067image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:34.366462image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:37.864927image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.635593image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.694828image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:50.489801image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:50:06.488502image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:30.842169image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:34.603826image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:38.063099image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:41.828453image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:45.901261image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-06-05T11:49:50.703717image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Missing values

2024-06-05T11:50:09.985510image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-05T11:50:10.480889image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-05T11:50:11.118883image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeLegislative DistrictDOL Vehicle IDVehicle LocationLatitudeLongitudeElectric Utility2020 Census Tract
0WAUTPBFF4HKingSeattleWA98126.02017AUDIA3Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range1634235085336POINT (-122.374105 47.54468)47.544680-122.374105CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033011500
1WAUUPBFF2JThurstonOlympiaWA98502.02018AUDIA3Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range1622237896795POINT (-122.943445 47.059252)47.059252-122.943445PUGET SOUND ENERGY INC53067011100
25YJSA1E22HThurstonLaceyWA98516.02017TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible21022154498865POINT (-122.78083 47.083975)47.083975-122.780830PUGET SOUND ENERGY INC53067012226
31C4JJXP62MThurstonTeninoWA98589.02021JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range2520154525493POINT (-122.85403 46.856085)46.856085-122.854030PUGET SOUND ENERGY INC53067012620
45YJ3E1EC9LYakimaYakimaWA98902.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible30814225996361POINT (-120.524012 46.5973939)46.597394-120.524012PACIFICORP53077000800
51C4JJXP66PThurstonOlympiaWA98501.02023JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range2122220675367POINT (-122.89692 47.043535)47.043535-122.896920PUGET SOUND ENERGY INC53067010802
61G1RA6S53HKitsapKeyportWA98345.02017CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible5323162720022POINT (-122.6250117 47.7021263)47.702126-122.625012PUGET SOUND ENERGY INC53035091100
75YJ3E1EB5LSnohomishMountlake TerraceWA98043.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible32216293899POINT (-122.30842 47.78416)47.784160-122.308420PUGET SOUND ENERGY INC53061051302
8WA1F2AFY1NKingSeattleWA98119.02022AUDIQ5Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range2336207620633POINT (-122.36731 47.6377681)47.637768-122.367310CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033005902
91G1RB6S59HThurstonOlympiaWA98501.02017CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible5322237392459POINT (-122.89692 47.043535)47.043535-122.896920PUGET SOUND ENERGY INC53067010700
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeLegislative DistrictDOL Vehicle IDVehicle LocationLatitudeLongitudeElectric Utility2020 Census Tract
181448KNDPZDAH7PWhatcomFerndaleWA98248.02023KIASPORTAGEPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible3442254987302POINT (-122.6011039 48.85324)48.853240-122.601104PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY53073010600
1814491N4BZ0CP6GFranklinPascoWA99301.02016NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible849194445051POINT (-119.0982 46.232395)46.232395-119.098200BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF FRANKLIN COUNTY53021020605
181450JTDKN3DPXDIslandGreenbankWA98253.02013TOYOTAPRIUS PLUG-INPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range610145854334POINT (-122.566915 48.089609)48.089609-122.566915PUGET SOUND ENERGY INC53029971302
181451KM8KRDAF3PPierceTacomaWA98407.02023HYUNDAIIONIQ 5Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched027228174334POINT (-122.5113356 47.2923828)47.292383-122.511336BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY53053060500
181452KNDPZDAH5PWhatcomFerndaleWA98248.02023KIASPORTAGEPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible3442235303008POINT (-122.6011039 48.85324)48.853240-122.601104PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY53073010503
18145350EA1TEA7PDouglasEast WenatcheeWA98802.02023LUCIDAIRBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched012244207316POINT (-120.28674 47.4176)47.417600-120.286740PUD NO 1 OF DOUGLAS COUNTY53017950400
1814541C4JJXP60NSpokaneSpokane ValleyWA99206.02022JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range214207180774POINT (-117.24549 47.6534)47.653400-117.245490BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY53063012402
1814555YJ3E1EA0MKingEnumclawWA98022.02021TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched031161934202POINT (-121.98953 47.20347)47.203470-121.989530PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)53033031302
1814565YJ3E1EC8LClarkVancouverWA98682.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible30818100859650POINT (-122.5286031 45.6686601)45.668660-122.528603BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)53011040604
1814575YJSA1E27FWhatcomBellinghamWA98225.02015TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible20842256731576POINT (-122.486115 48.761615)48.761615-122.486115PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY53073000401